Evidence Traceability for Health AI and Biosecurity: A Data Governance Architecture for Reproducibility and Audit

Abstract: Health AI and biosecurity analytics increasingly influence clinical triage, surveillance, supply allocation, and emergency governance, yet many deployments remain difficult to reproduce, audit, or justify when challenged by regulators, clinicians, or the public. This manuscript develops an evidence-traceability architecture that treats data governance as an epistemic system: every claim produced by an AI pipeline is paired with a verifiable trail of provenance, transformations, assumptions, evaluations, and decision authorizations. The central argument is that trustworthy health AI cannot be secured by model performance alone; it requires a discipline of traceability that unifies research design under uncertainty, statistical explanation and causal inference, predictive learning with trust constraints, and scalable engineering practices comparable to safety-critical MLOps. Across five chapters, the book formalizes a traceability model (evidence objects, lineage graphs, and audit checkpoints), derives evaluation logic that links clinical and public-health objectives to metrics and thresholds, and translates the framework into sectoral playbooks spanning South Asia, Europe, Africa, and the Americas. The research outputs are governance-ready: template registries for assumptions and data use, reproducibility artifacts for end-to-end reconstruction, audit protocols for drift and bias, and policy instruments for procurement, accountability, and incident response. The result is a publisher-ready framework for building health AI systems whose conclusions can be defended, reproduced, and revised transparently under deep uncertainty. Keywords evidence traceability, health AI, biosecurity analytics, data governance, reproducibility, auditability, provenance, lineage, uncertainty quantification, causal inference, statistical modeling, trustworthy machine learning, model monitoring, drift detection, MLOps, privacy and confidentiality, clinical decision support, public health surveillance, risk management, policy compliance